Getting Started
Install R Packages
packages = c('tidyverse','sf','tmap','lubridate','clock','sftime','rmarkdown')
for(p in packages){
if(!require(p, character.only = T)){
install.packages(p)
}
library(p, character.only = T)
}
Importing the data
schools <- read_sf("/Users/junqiuni/Downloads/visual/pilipalabong/ISSS608/in_class_ex/data/wkt/Schools.csv",
options = "GEOM_POSSIBLE_NAMES=location")
buildings<- read_sf("/Users/junqiuni/Downloads/visual/pilipalabong/ISSS608/in_class_ex/data/wkt/Buildings.csv",
options = "GEOM_POSSIBLE_NAMES=location")
apartments <- read_sf("/Users/junqiuni/Downloads/visual/pilipalabong/ISSS608/in_class_ex/data/wkt/Apartments.csv",
options = "GEOM_POSSIBLE_NAMES=location")
pubs <- read_sf("/Users/junqiuni/Downloads/visual/pilipalabong/ISSS608/in_class_ex/data/wkt/Pubs.csv",
options = "GEOM_POSSIBLE_NAMES=location")
employers <- read_sf("/Users/junqiuni/Downloads/visual/pilipalabong/ISSS608/in_class_ex/data/wkt/employers.csv",
options = "GEOM_POSSIBLE_NAMES=location")
restaurants <- read_sf("/Users/junqiuni/Downloads/visual/pilipalabong/ISSS608/in_class_ex/data/wkt/Restaurants.csv",
options = "GEOM_POSSIBLE_NAMES=location")
view the deometry type
Simple feature collection with 4 features and 4 fields
Geometry type: POINT
Dimension: XY
Bounding box: xmin: -4701.463 ymin: 1607.984 xmax: -376.7505 ymax: 6556.032
CRS: NA
# A tibble: 4 × 5
schoolId monthlyCost maxEnrollment location
<chr> <chr> <chr> <POINT>
1 0 12.81244502 242 (-376.7505 1607.984)
2 450 91.14351385 418 (-2597.448 3194.155)
3 900 38.00537955 394 (-2539.158 6556.032)
4 1350 73.19785215 384 (-4701.463 5141.763)
# … with 1 more variable: buildingId <chr>
tmap plot
tmap_mode("plot")
tm_shape(buildings)+
tm_polygons(col = "grey60",
size = 1,
border.col = "black",
border.lwd = 1)
Interactive tmap view
tmap_mode("view")
tm_shape(buildings)+
tm_polygons(col = "grey60",
size = 1,
border.col = "black",
border.lwd = 1)
Composite map with tmap
tmap_mode("plot")
tm_shape(buildings)+
tm_polygons(col = "grey60",
size = 1,
border.col = "black",
border.lwd = 1) +
tm_shape(employers) +
tm_dots(col = "red")
tmap_mode("plot")
tm_shape(buildings)+
tm_polygons(col = "grey60",
size = 1,
border.col = "black",
border.lwd = 1) +
tm_shape(employers) +
tm_dots(col = "red") +
tm_shape(schools) +
tm_dots(col = "yellow") +
tm_shape(pubs) +
tm_dots(col = "blue") +
tm_shape(restaurants) +
tm_dots(col = "green") +
tm_shape(apartments) +
tm_dots(col = "lightblue")
Movement Data
logs <- read_sf("/Users/junqiuni/Downloads/visual/pilipalabong/ISSS608/in_class_ex/data/wkt/ParticipantStatusLogs1.csv",
options = "GEOM_POSSIBLE_NAMES=currentLocation")
logs_selected <- logs %>%
mutate(Timestamp = date_time_parse(timestamp,
zone="",
format="%Y-%m-%dT%H:%M:%S"))%>%
mutate(day=get_day(Timestamp)) %>%
filter(currentMode == 'Transport')
write_rds(logs_selected,"/Users/junqiuni/Downloads/visual/pilipalabong/ISSS608/in_class_ex/data/rds/logs_selected.rds")
Importing the data
logs_selected <- read_rds("/Users/junqiuni/Downloads/visual/pilipalabong/ISSS608/in_class_ex/data/rds/logs_selected.rds")
tmap_mode("plot")
tm_shape(buildings)+
tm_polygons(col = "grey60",
size = 1,
border.col = "black",
border.lwd = 1) +
tm_shape(logs_selected) +
tm_dots(col = "red")
hex <- st_make_grid(buildings,
cellsize=100,
square=FALSE) %>%
st_sf() %>%
rowid_to_column('hex_id')
plot(hex)
points_in_hex <- st_join(logs_selected,
hex,
join=st_within)
#plot(points_in_hex, pch='.')
points_in_hex <- st_join(logs_selected,
hex,
join=st_within) %>%
st_set_geometry(NULL) %>%
count(name='pointCount', hex_id)
head(points_in_hex)
# A tibble: 6 × 2
hex_id pointCount
<int> <int>
1 169 35
2 212 56
3 225 21
4 226 94
5 227 22
6 228 45
hex_combined <- hex %>%
left_join(points_in_hex,
by = 'hex_id') %>%
replace(is.na(.), 0)
tm_shape(hex_combined %>%
filter(pointCount > 0))+
tm_fill("pointCount",
n = 8,
style = "quantile") +
tm_borders(alpha = 0.1)
Movement Path
logs_path <- logs_selected %>%
group_by(participantId, day) %>%
summarize(m = mean(Timestamp),
do_union=FALSE) %>%
st_cast("LINESTRING")
Simple feature collection with 5781 features and 3 fields
Geometry type: LINESTRING
Dimension: XY
Bounding box: xmin: -4616.828 ymin: 35.4377 xmax: 2630 ymax: 7836.546
CRS: NA
# A tibble: 5,781 × 4
# Groups: participantId [1,011]
participantId day m currentLocation
<chr> <int> <dttm> <LINESTRING>
1 0 1 2022-03-01 13:34:23 (-2721.353 6862.861, -2689…
2 0 2 2022-03-02 14:19:50 (-2721.353 6862.861, -2689…
3 0 3 2022-03-03 13:39:13 (-2721.353 6862.861, -2689…
4 0 4 2022-03-04 13:38:11 (-2721.353 6862.861, -2689…
5 0 5 2022-03-05 13:08:02 (-2721.353 6862.861, -2689…
6 0 6 2022-03-06 06:28:00 (-2721.353 6862.861, -2689…
7 1 1 2022-03-01 18:07:24 (-1531.133 5597.244, -1863…
8 1 2 2022-03-02 16:57:05 (-2619.036 5860.49, -2200.…
9 1 3 2022-03-03 14:13:40 (-260.4575 5026.151, -352.…
10 1 4 2022-03-04 14:31:45 (-3903.194 5967.837, -3655…
# … with 5,771 more rows
logs_path0=filter(logs_path,participantId == 0)
tmap_mode("plot")
tm_shape(buildings)+
tm_polygons(col = "grey60",
size = 1,
border.col = "black",
border.lwd = 1) +
tm_shape(logs_path0) +
tm_lines(col = "blue")
